{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experiment: \n",
    "\n",
    "Evaluate pruning by magnitude weighted by coactivations (more thorough evaluation), compare it to baseline (SET), in GSC. Applied only to linear layers\n",
    "\n",
    "#### Motivation.\n",
    "\n",
    "Check if results are consistently above baseline.\n",
    "\n",
    "#### Conclusion\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# experiment configurations\n",
    "base_exp_config = dict(\n",
    "    device=\"cuda\",\n",
    "    # dataset related\n",
    "    dataset_name=\"PreprocessedGSC\",\n",
    "    data_dir=os.path.expanduser(\"~/nta/datasets/gsc\"),\n",
    "    batch_size_train=(4, 16),\n",
    "    batch_size_test=1000,\n",
    "    # network related\n",
    "    # test 2 networks - create a new one equivalent\n",
    "    # need to load into notebook and found out size\n",
    "    network=tune.grid_search([\"GSCHeb\", \"GSCHebSmall\"]),\n",
    "    optim_alg=\"SGD\",\n",
    "    momentum=0.9, # 0,\n",
    "    learning_rate=0.01, # 0.1,\n",
    "    weight_decay= 0.01, # 1e-4,\n",
    "    lr_scheduler=\"MultiStepLR\",\n",
    "    lr_milestones=[30, 60, 90],\n",
    "    lr_gamma=0.9, # 0.1,\n",
    "    use_kwinners=True,\n",
    "    # sparse_linear_only=True, # False\n",
    "    # model related\n",
    "    model=\"BaseModel\",\n",
    "    # on_perc=0.04,\n",
    "    # sparse related\n",
    "    hebbian_prune_perc=None,\n",
    "    hebbian_grow=False,\n",
    "    # weight_prune_perc=tune.grid_search(list(np.arange(0, 1.001, 0.05))),\n",
    "    # pruning_early_stop=2,\n",
    "    # additional validation\n",
    "    test_noise=False,\n",
    "    # debugging\n",
    "    debug_weights=True,\n",
    "    # debug_sparse=False,\n",
    ")\n",
    "\n",
    "# ray configurations\n",
    "tune_config = dict(\n",
    "    name=__file__.replace(\".py\", \"\") + \"_test1\",\n",
    "    num_samples=10,\n",
    "    local_dir=os.path.expanduser(\"~/nta/results\"),\n",
    "    checkpoint_freq=0,\n",
    "    checkpoint_at_end=False,\n",
    "    stop={\"training_iteration\": 100},\n",
    "    resources_per_trial={\"cpu\": 1, \"gpu\": .25},\n",
    "    loggers=DEFAULT_LOGGERS,\n",
    "    verbose=0,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import tabulate\n",
    "import pprint\n",
    "import click\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ray.tune.commands import *\n",
    "from nupic.research.frameworks.dynamic_sparse.common.browser import *\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from scipy.ndimage.filters import gaussian_filter1d\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.set_palette(\"colorblind\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and check data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "exps = ['comparison_pruning_2' , 'comparison_iterative_pruning_2', 'comparison_set_2']\n",
    "paths = [os.path.expanduser(\"~/nta/results/{}\".format(e)) for e in exps]\n",
    "df = load_many(paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experiment Name</th>\n",
       "      <th>train_acc_max</th>\n",
       "      <th>train_acc_max_epoch</th>\n",
       "      <th>train_acc_min</th>\n",
       "      <th>train_acc_min_epoch</th>\n",
       "      <th>train_acc_median</th>\n",
       "      <th>train_acc_last</th>\n",
       "      <th>val_acc_max</th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th>val_acc_min</th>\n",
       "      <th>...</th>\n",
       "      <th>start_pruning_epoch</th>\n",
       "      <th>target_final_density</th>\n",
       "      <th>weight_decay</th>\n",
       "      <th>experiment_type</th>\n",
       "      <th>first_run</th>\n",
       "      <th>hebbian_grow</th>\n",
       "      <th>hebbian_prune_perc</th>\n",
       "      <th>iterative_pruning_schedule</th>\n",
       "      <th>pruning_early_stop</th>\n",
       "      <th>weight_prune_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_target_final_density=0.1</td>\n",
       "      <td>0.95418</td>\n",
       "      <td>199</td>\n",
       "      <td>0.28814</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89213</td>\n",
       "      <td>0.95418</td>\n",
       "      <td>0.8692</td>\n",
       "      <td>170</td>\n",
       "      <td>0.4052</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1_target_final_density=0.15</td>\n",
       "      <td>0.96978</td>\n",
       "      <td>193</td>\n",
       "      <td>0.25938</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89539</td>\n",
       "      <td>0.96880</td>\n",
       "      <td>0.8789</td>\n",
       "      <td>160</td>\n",
       "      <td>0.3710</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2_target_final_density=0.2</td>\n",
       "      <td>0.97864</td>\n",
       "      <td>195</td>\n",
       "      <td>0.30640</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89862</td>\n",
       "      <td>0.97856</td>\n",
       "      <td>0.8770</td>\n",
       "      <td>167</td>\n",
       "      <td>0.4360</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3_target_final_density=0.25</td>\n",
       "      <td>0.98144</td>\n",
       "      <td>195</td>\n",
       "      <td>0.29424</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89384</td>\n",
       "      <td>0.98078</td>\n",
       "      <td>0.8779</td>\n",
       "      <td>190</td>\n",
       "      <td>0.4156</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4_target_final_density=0.3</td>\n",
       "      <td>0.98132</td>\n",
       "      <td>196</td>\n",
       "      <td>0.26182</td>\n",
       "      <td>0</td>\n",
       "      <td>0.88829</td>\n",
       "      <td>0.98000</td>\n",
       "      <td>0.8728</td>\n",
       "      <td>184</td>\n",
       "      <td>0.3888</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Experiment Name  train_acc_max  train_acc_max_epoch  \\\n",
       "0   0_target_final_density=0.1        0.95418                  199   \n",
       "1  1_target_final_density=0.15        0.96978                  193   \n",
       "2   2_target_final_density=0.2        0.97864                  195   \n",
       "3  3_target_final_density=0.25        0.98144                  195   \n",
       "4   4_target_final_density=0.3        0.98132                  196   \n",
       "\n",
       "   train_acc_min  train_acc_min_epoch  train_acc_median  train_acc_last  \\\n",
       "0        0.28814                    0           0.89213         0.95418   \n",
       "1        0.25938                    0           0.89539         0.96880   \n",
       "2        0.30640                    0           0.89862         0.97856   \n",
       "3        0.29424                    0           0.89384         0.98078   \n",
       "4        0.26182                    0           0.88829         0.98000   \n",
       "\n",
       "   val_acc_max  val_acc_max_epoch  val_acc_min  ...  start_pruning_epoch  \\\n",
       "0       0.8692                170       0.4052  ...                  1.0   \n",
       "1       0.8789                160       0.3710  ...                  1.0   \n",
       "2       0.8770                167       0.4360  ...                  1.0   \n",
       "3       0.8779                190       0.4156  ...                  1.0   \n",
       "4       0.8728                184       0.3888  ...                  1.0   \n",
       "\n",
       "   target_final_density  weight_decay experiment_type  first_run hebbian_grow  \\\n",
       "0                  0.10        0.0005             NaN        NaN          NaN   \n",
       "1                  0.15        0.0005             NaN        NaN          NaN   \n",
       "2                  0.20        0.0005             NaN        NaN          NaN   \n",
       "3                  0.25        0.0005             NaN        NaN          NaN   \n",
       "4                  0.30        0.0005             NaN        NaN          NaN   \n",
       "\n",
       "   hebbian_prune_perc  iterative_pruning_schedule  pruning_early_stop  \\\n",
       "0                 NaN                         NaN                 NaN   \n",
       "1                 NaN                         NaN                 NaN   \n",
       "2                 NaN                         NaN                 NaN   \n",
       "3                 NaN                         NaN                 NaN   \n",
       "4                 NaN                         NaN                 NaN   \n",
       "\n",
       "  weight_prune_perc  \n",
       "0               NaN  \n",
       "1               NaN  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56, 50)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Experiment Name', 'train_acc_max', 'train_acc_max_epoch',\n",
       "       'train_acc_min', 'train_acc_min_epoch', 'train_acc_median',\n",
       "       'train_acc_last', 'val_acc_max', 'val_acc_max_epoch', 'val_acc_min',\n",
       "       'val_acc_min_epoch', 'val_acc_median', 'val_acc_last', 'val_acc_all',\n",
       "       'epochs', 'experiment_file_name', 'trial_time', 'mean_epoch_time',\n",
       "       'augment_images', 'checkpoint_dir', 'data_dir', 'dataset_name',\n",
       "       'device', 'end_pruning_epoch', 'input_size', 'learning_rate',\n",
       "       'local_dir', 'lr_gamma', 'lr_milestones', 'lr_scheduler', 'model',\n",
       "       'momentum', 'name', 'nesterov_momentum', 'network', 'num_classes',\n",
       "       'on_perc', 'optim_alg', 'sparse_end', 'sparse_start',\n",
       "       'start_pruning_epoch', 'target_final_density', 'weight_decay',\n",
       "       'experiment_type', 'first_run', 'hebbian_grow', 'hebbian_prune_perc',\n",
       "       'iterative_pruning_schedule', 'pruning_early_stop',\n",
       "       'weight_prune_perc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PruningModel', 'IterativePruningModel', 'SET'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['model'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate density for each model\n",
    "df.loc[df['model'] == 'PruningModel', 'density'] = df.loc[df['model'] == 'PruningModel', 'target_final_density']\n",
    "df.loc[df['model'] == 'IterativePruningModel', 'density'] = df.loc[df['model'] == 'IterativePruningModel', 'target_final_density']\n",
    "df.loc[df['model'] == 'SET', 'density'] = df.loc[df['model'] == 'SET', 'on_perc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiment Details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Did any  trials failed?\n",
    "num_epochs = 200\n",
    "df[df[\"epochs\"]<num_epochs][\"epochs\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56, 51)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing failed or incomplete trials\n",
    "df_origin = df.copy()\n",
    "df = df_origin[df_origin[\"epochs\"]>=30]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper functions\n",
    "def mean_and_std(s):\n",
    "    return \"{:.3f} ± {:.3f}\".format(s.mean(), s.std())\n",
    "\n",
    "def round_mean(s):\n",
    "    return \"{:.0f}\".format(round(s.mean()))\n",
    "\n",
    "stats = ['min', 'max', 'mean', 'std']\n",
    "\n",
    "def agg(columns, filter=None, round=3):\n",
    "    if filter is None:\n",
    "        return (df.groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n",
    "    else:\n",
    "        return (df[filter].groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>round_mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.10</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>172</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>170</td>\n",
       "      <td>0.869</td>\n",
       "      <td>0.869</td>\n",
       "      <td>0.869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>161</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.15</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>187</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.831</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>160</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>168</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.20</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>167</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>167</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>192</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.25</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>162</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>190</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>168</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.30</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>194</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>184</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.873</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>164</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.35</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>185</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>196</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.881</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>165</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.40</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>195</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>165</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>187</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.45</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>176</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.831</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>185</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>181</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.50</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>191</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>166</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>187</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.55</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>183</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>168</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.884</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>168</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.60</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>173</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>192</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>183</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.65</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>166</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>195</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>175</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.70</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>191</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>172</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.884</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>188</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.75</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>175</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>174</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>194</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.80</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>194</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.831</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>172</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>166</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.85</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>161</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>180</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>194</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.90</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>196</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>199</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.884</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>190</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.884</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.95</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>167</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>188</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>197</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.00</th>\n",
       "      <th>IterativePruningModel</th>\n",
       "      <td>171</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PruningModel</th>\n",
       "      <td>176</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              val_acc_max_epoch val_acc_max                    \\\n",
       "                                     round_mean         min    max   mean std   \n",
       "density model                                                                   \n",
       "0.10    IterativePruningModel               172       0.836  0.836  0.836 NaN   \n",
       "        PruningModel                        170       0.869  0.869  0.869 NaN   \n",
       "        SET                                 161       0.863  0.863  0.863 NaN   \n",
       "0.15    IterativePruningModel               187       0.831  0.831  0.831 NaN   \n",
       "        PruningModel                        160       0.879  0.879  0.879 NaN   \n",
       "        SET                                 168       0.878  0.878  0.878 NaN   \n",
       "0.20    IterativePruningModel               167       0.834  0.834  0.834 NaN   \n",
       "        PruningModel                        167       0.877  0.877  0.877 NaN   \n",
       "        SET                                 192       0.879  0.879  0.879 NaN   \n",
       "0.25    IterativePruningModel               162       0.828  0.828  0.828 NaN   \n",
       "        PruningModel                        190       0.878  0.878  0.878 NaN   \n",
       "        SET                                 168       0.883  0.883  0.883 NaN   \n",
       "0.30    IterativePruningModel               194       0.834  0.834  0.834 NaN   \n",
       "        PruningModel                        184       0.873  0.873  0.873 NaN   \n",
       "        SET                                 164       0.882  0.882  0.882 NaN   \n",
       "0.35    IterativePruningModel               185       0.832  0.832  0.832 NaN   \n",
       "        PruningModel                        196       0.881  0.881  0.881 NaN   \n",
       "        SET                                 165       0.888  0.888  0.888 NaN   \n",
       "0.40    IterativePruningModel               195       0.832  0.832  0.832 NaN   \n",
       "        PruningModel                        165       0.883  0.883  0.883 NaN   \n",
       "        SET                                 187       0.882  0.882  0.882 NaN   \n",
       "0.45    IterativePruningModel               176       0.831  0.831  0.831 NaN   \n",
       "        PruningModel                        185       0.877  0.877  0.877 NaN   \n",
       "        SET                                 181       0.886  0.886  0.886 NaN   \n",
       "0.50    IterativePruningModel               191       0.830  0.830  0.830 NaN   \n",
       "        PruningModel                        166       0.883  0.883  0.883 NaN   \n",
       "        SET                                 187       0.882  0.882  0.882 NaN   \n",
       "0.55    IterativePruningModel               183       0.825  0.825  0.825 NaN   \n",
       "        PruningModel                        168       0.884  0.884  0.884 NaN   \n",
       "        SET                                 168       0.886  0.886  0.886 NaN   \n",
       "0.60    IterativePruningModel               173       0.830  0.830  0.830 NaN   \n",
       "        PruningModel                        192       0.882  0.882  0.882 NaN   \n",
       "        SET                                 183       0.883  0.883  0.883 NaN   \n",
       "0.65    IterativePruningModel               166       0.834  0.834  0.834 NaN   \n",
       "        PruningModel                        195       0.880  0.880  0.880 NaN   \n",
       "        SET                                 175       0.886  0.886  0.886 NaN   \n",
       "0.70    IterativePruningModel               191       0.833  0.833  0.833 NaN   \n",
       "        PruningModel                        172       0.884  0.884  0.884 NaN   \n",
       "        SET                                 188       0.885  0.885  0.885 NaN   \n",
       "0.75    IterativePruningModel               175       0.836  0.836  0.836 NaN   \n",
       "        PruningModel                        174       0.883  0.883  0.883 NaN   \n",
       "        SET                                 194       0.883  0.883  0.883 NaN   \n",
       "0.80    IterativePruningModel               194       0.831  0.831  0.831 NaN   \n",
       "        PruningModel                        172       0.885  0.885  0.885 NaN   \n",
       "        SET                                 166       0.883  0.883  0.883 NaN   \n",
       "0.85    IterativePruningModel               161       0.834  0.834  0.834 NaN   \n",
       "        PruningModel                        180       0.886  0.886  0.886 NaN   \n",
       "        SET                                 194       0.882  0.882  0.882 NaN   \n",
       "0.90    IterativePruningModel               196       0.832  0.832  0.832 NaN   \n",
       "        PruningModel                        199       0.884  0.884  0.884 NaN   \n",
       "        SET                                 190       0.884  0.884  0.884 NaN   \n",
       "0.95    IterativePruningModel               167       0.836  0.836  0.836 NaN   \n",
       "        PruningModel                        188       0.883  0.883  0.883 NaN   \n",
       "        SET                                 197       0.886  0.886  0.886 NaN   \n",
       "1.00    IterativePruningModel               171       0.834  0.834  0.834 NaN   \n",
       "        PruningModel                        176       0.885  0.885  0.885 NaN   \n",
       "\n",
       "                              model  \n",
       "                              count  \n",
       "density model                        \n",
       "0.10    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.15    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.20    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.25    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.30    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.35    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.40    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.45    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.50    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.55    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.60    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.65    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.70    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.75    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.80    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.85    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.90    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "0.95    IterativePruningModel     1  \n",
       "        PruningModel              1  \n",
       "        SET                       1  \n",
       "1.00    IterativePruningModel     1  \n",
       "        PruningModel              1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg(['density', 'model'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot accuracy over epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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P+liap87bTyOrD8/lselfdXvWXffpdN96QapDvZsdq7RMTefxv58Zp7w5U+bU3hlXq9VUKpUMbViZ7tsuyOC6e9PUNisdxUsyZc6xqQ4P7XRCSXV4KP0rbhwJdy9P70O/2PkzutKcKfNPHZm5e3baF5097r9HJwLr4E481epwBlbfUZu5+3Bt9m7/ipu2Gc5vqbljYdoOGgl3F5yWoa4Hs/wnOwprK1nwvO9n6qFPNRGtDoS5k4Awd2IZ7u/Kuus/m7VLPpHhvtWbHWudd0Jmn/neTDvyuT75xHYN9izLqsvelZ7bvz6m9oe9abVPStNQhvu7s+oX707XDZ8fU/vmGUdkwXO+nf5Hrk3vA5dmw/2XZqj7gR2eI9ylEXhBBdA4PJN33WDPw1l33aez7sYvbfXyt2nKzEw/+Q2Zecqb0/Pr72XVz9+V6kD3Zm1aZh2VBb/9rbTOPX5MHy4d7lubgTV3ZWD1HY8GvSNf72yG2LZV0jL90NqavLOPrq3TOxL6tsw4zM+G2ZUyyi8aKaN8tncV+1i1Wk1P+Y2s/NnbMty7cuP+pqkHZO6TP5GOY148bv+beC5vX7VaTe/9P83aX30qG+750VbHmzsWZsbJb8z0E/8ozVPnbreP5NE1L8cS4u5oPINr7qzN2h0Jd8dS+rV1zrG1f5cvOjtti87xIZB9yDq4k8vwwPr0P/Kr2uzdpVenb+k1Geq6vy59ty96Yg568SV16WuyE+ZOAsLciWm4b23W/uqTWXvdp7b6h/GUA07J7LPel6lHPGvc/lBOfQ2tX56eu7+Xnju/nd77/29Mn7ZKRmfmrvF9RMPY8MBlWXHRazf7h19T2+y0LTo7G37z46Q6uFn7KQeckgW/8+00dyzc+A/ParWawXW/Se8DPxfu0tC8oAJoHJ7JYzew+s6sXfKJdN3271uV6GzuWJiZj/vTTD/hj9LUNmPj/uGBnnTfekH6V9yYStOUTD3iGZl62G8l1eoez+SoVqsZXv/Io7N4V99RC3nX3JnBNXenOtS36502taZ1ZH3eli3KNzd3HDTh//2kjPL4M7R+eVZe+vb0lN/YbP+0x/x25p736bR0LtpPI9t9nstbqw72pbv8RtZe98kMrLh5q+NT5p+amaf+aS3E38+zJwe7H3y0LPNDl2dgxS3ZWRn95umHbizL3L7o7LTOOW7CP2/3paH1y9N9x7fSfdvX0r/s2m22sQ7u5DHY/dCja+8+fHX6HlmS6kDPbvV1yGt+k5bp4+//M41GmDsJCHMntqHeVVm75ONZd/1nt3qgth14Rmad9b5MPfR8P9xMQkM9y9Jz93fTc+eF6X3g0jEHuFuaethvZe55n07rzCPqPEIYu+GBnqz+xV9l3fWf2Wz/tMf8duac95m0di7M0Prl6brlKxlYfWeaWjsy7egXZurBT9zpJ4iFuzQqL6gAGodn8s71LVuSNdf+fdbf+d/Z8oV86+xjMvO0d6SzeFkqLW3bPL9aHd44A7c6PLhPfraqDg9lsOv+WrC7enQmb21W7+C6e7ZZKnhnKq0dm8/inX10Wmcdk9bZR43rmUvDg72blFG+SBnlcWr9r/8nK3765gx1P7hxX2XKjMx54kcz/YQ/HFfvjjyXHzW0fnnW3fjFdN34xQytX7bF0UqmPea3M+Nxb0n7oic27H/jod7V6Xvoio0zd/uWXZsMD+7wnKb2uWlf+IRaaeZF52TKAaek0ty6j0Y8MWy2Du69P9nmn7l1cElqP5sNrLw1vUuvSd/DV6X3gf/L4Lptz9re0sKXX5W2+afu5RFOfMLcSUCYOzkMrV+eNdd+LF03fH6rNTDaFp2T2We+L1MPedJ+Gh37ymDPw1l/13dqAe6Dl2335UPbQWemadqCbLj7u2Pqt9IyNbPPel9mnPqnAiv2ud4Hf5HlF702g2vu2rivqW1WrSzYsS9/tOxTncpACXdpFF5QATQOz+Rtq1ar6b3vkqy59mPpvf+nWx1vW3B6Zp7+/zLtyN8ZU6nkRlId7MvAul9nYPVdGdw4q7cW9A71PLxbfTa1z91sFu/o71tmHZWm1ml1voOtbfnz8ejamNttr4zyhDTctzarLn93um760mb72w9+cuad/7m0zjpqP41s13guJ/0rb8na6z6dntu+tlWVgUrLtEx/7Ksy45Q3pXX20ftphLtveGB9reTraLj78C93Oiuw0tqRtoPOHFlz95y0HXjGPnm2jjdjXgf3iGel87hXWgeXbRpYfUce+LcTxtCykkNee09aOg7a62Oa6IS5k4Awd3IZ7Hk4a6/5u6y76ctblbRqP+QpmX3W+9O+8Kz9NDr2hsGuB9Jz13fSc9eF6XvwF9l2WZpK2hY+IR1HvyAdRz0/LdMPTpKsvupvsuaqD2/1ybumaQdm6sHnpueO/9ysvynzT82887/g01TsE8MD67P6ivdl3XWfyqbfh1OPeFbmPfVzaelcuE/GIdxlf/GCCqBxeCZvrjo8lJ67Lszaaz+W/keu2+r41MOenpmnvSPtB587IcO94f6uzWbxDqwendl7Z4b71uxWn82dB28j6D0qLTOO2OOZZqMh7ob7LknPXf+dan9PWmYdlekn/mFaOg7aKtRVRnly2PDAz7Pi4jds9qHZ8fRB7sn6XK5Wq9lw38VZ96tP1mZSbqG5c1FmnPKmTD/hNWlun70fRrh3VIcG0r/8hvQ+dPnG8sybrgO9TU2taZv/uFpp5kXnpG3hE8Z1hYQ9ZR1c6u3B/3jCdktyj2o/9Kk56AVbr93NrhPmTgLC3MlpsOv+rLn6b9N1y1e2CuqmHvb0zD7rfWk78LT9NDr21GDX/em588L03Hlh+h6+cjutKmlf9MR0HP2CTDvqedsNvobWP5J1N/1TBlbcnEpzW6Ye8cx0HP2CpFJJ39IlWXHJGzZfa6XSnJmPe8v/Z+++46So7/+Bv2Zne79+xx1IO5ZylKOJqIAK9opGjVI0lhiNvftLbIn5xtgL9tjAEqNYYhRRooAgvUhdQEC4ytXd215mfn/s3t4tV4G729u71/PxyOPCZ2Zn35vA3M685vP+wDrpQT7hSJ3GV/ITKhZfEz8bV21B6tSnYBw+O6E3BRnuUlfprTeoiIi6I56TI6SQD67t78Kx/hmEHL/EbxQUMAy5FJbxd0KTMToxBSaYLMuQfFWNZvHuigS9tXsQrN0DOeQ98oMKIpSWAVClDGkIeq2DI+vzGnPbnPEsS2GEXEUo/2ImgpVbmhzbXHgL0qY8jnDQC+/e/7CNci8jhbyoXfUXONY/E7c0kzprHDKmvwp1xqgEVte63nZelkI+uHe+D8fG5xGs2t5kuzprHCyFt8KQf3GvaDUsyzKCNTvhK/oxGvCuQLjuQJuvU6WNgDb35FjAm4zrRR8JroNLnUWWwvDuX4TyL2aixfWuBRHZMxdBm3vSUXXOo3gMc3sBhrm9W9CxH7Vr/gbX9vlN1kzVDzwX1hMe6rUX2skm6NgPz55ogFu2pvmdBAW0eVNhGDwT+sEXQGnIbvO4bbWmlcNBONY/jdrVf41r26M0D0D6afOgO276MXwqonhSyIvanx6J3ExoPBv3uDOQPv3l2Kzy7kSWZYQce+ErWgZv0VL4ipYx3KUO0dtuUBERdWe9/Zwc9tWi7udX4dz0YpP1GAVRC2PB1bCMvQ0qy4AEVdj9ybKEsKu4UdC7JzqrdxdCjn1NrtfbQ1DqIrN36wNe65DYrF6FNg2CIEAK1KF4wbjIGsAtUGcWIlS7l22UezF/+QZUfns9ApU/NwwqlLBOuAfWCfe3uNZ1IvWW83LYXQ7nz6/C+fOrkLwVh20VoB90Pixjb4Omz+Re/+8z5DwQC3Z9xT8iWL2jzdcozQMazdw9EaqUIUn/v2O71sHVpETWwR02i+vg0jGp2/oWqr6/pdlW7+kzXoPRdmmCKut5GOb2AgxzCQCCNbtRs/oxuHd+gMOfltHnz0TKpD9DnTYiMcVRi4K1v0Rm4O5ZiED5+uZ3EkRo+54SaaE86HyI+syjeq9t27ZBlmUUFDS/3kGwZjcql9wEX9EPcePGYVcidcoTEHXpR/W+RPV8pWtQufgaBGvssTFBbULa1KdgHD43aS4uGO5SR+ktN6iIiJJBbz0nh1wlcG58Hs4tr0MO1MVtU2hSYB7zB5hH3wRRn5GgCnsGORxEyLmvIeRt1L65re+RLVFoUqBMyQekEAKHNhzx67V9T4Fx+GwYBl/ENsq9RORB7qdQs/qvcct2qVKHIn36a9D2mZTA6prq6eflQOUWODY8D5f9gybLqAkqY8N6uJxJ2aKwtxK+kpXRtsw/RpYFaOPBGYU+E9o+k6Ozd0+COn1UUswojKyDuwKuHQvasQ7uldD3P6tbPqRBySnsq0Hd1jej3SMFaHNPgmnE1VBozIkurUdhmNsLMMylxgLVO1D701/g3v3xYVsEGGyXIWXSn6BKGZKQ2igiWLM72kL5EwQqNjW/k0IJXd9TYci/GPpB53VIkNqeCyFZluHa/g6ql90LyV/TUI4uHWlTnoBh6BVJE7hR9yGFfKhd9Sgc65+Oa+em7TcdGdNfgdLcL4HVHbum4e5ShF3Frb6G4S4BPf8GFRFRMult5+RAtR2O9U/DtfO9JiGCaMyDZeytMBVcA4XamKAKew8p6Eawdk+kVXNsVm/kZ5vrRR4hpWUAjMPnsI1yLxeo3onKb39/2JJOAsxj/oiUyY90m3/3PfG8LMsSvPsXw7HxOfgOLGmyXTT1hWXMTTCO+B1ErTUBFSY3KeCCv2x1bM1df9nqNtvfC2oTtDknQJt7IjR9ToImewIUSm0XVdy2QLUdrp3vwb3jA66DSwkTyQnl2NIPsiy1uQwEHTmGub0Aw1xqTqDiZ9SsehSeX76I3yAoYBw2C9bjH4DKMjAxxfVCgeqdcO9eCM/uTxA4fB2jegoVdP2mRwPcczv8C9iRXAiF3eWoWnoH3Lv+HTeuO+50pJ36AturUbv5y9ahYvE1ce2PBJURqVP+AVPBNT3y4QCGu9RePfEGFRFRsuot52R/2VrUrnsCnj2f4/COTqrUobCMuwvGoZdDENWJKZDihH3VCNbsQbB2F0KNg97aPZCD7iM6lmHolcg4480e+f2bjpwsS6jb/AqqV/wJctAVG1eajkPa9JegP25GAquL6EnnZSnkhWvHe3BueC6uU1U9TfZEmAtvgSF/Jq8DO5AcDsB/aGM03F0Of8lKSP7a1l8kqqHJGg9tn0hrZm2fyVBoLEdfQ6Olzg5f9qwlXAeXuqvt2yPreQ8fPjzBlfRMDHN7AYa51Bp/+QbU/PQIvPu/jt+gUMI0fC6sE+9P+llx3VWgaltsBm6wanvzO4lq6I+bAf3gmdAPPK9Tn7w8mgshz76vUPm/mxGuOxgbE5R6pJzwEMyFN/Mig1okh/yoWf1XONY9GdfmSNv3VKTPeLVXzQQ4unDXDG3uSdDlTYE2byrUGaP5760H6kk3qIiIkl1PPifLsgzvr4vhWPckfEVLm2zX5EyCZfzd0A88h7MskoQsywi7SxGs2Y1D/728XTN4U09+HOaxt/L/Y4oTdP6KqiV/hPfXb+LGjcPnIHXKPxI6y68nnJdD7lLUbX4Fzp9fa/rvVFBAP/hCWApv63YtrnsqWZYQrNoWW3PXV7KizWtzQIA6Y1Q02D0RmtyToDRkt/1e0eDWW7QUvgP/gyyHocmaAP2gcwEomjxYI4V88Oz9D9fBpW6tJ5yXuzOGub0Aw1xqD1/JKtSsehS+A9/FbxDVMBVcA+uEe6E09klMcT2ELMsIVm6Be89CuHcvRLB6Z7P7CaIGuv5nwJA/E/oB53bZ+gJH+wtXCtShZuVDcG6ah8ZP76szxyJ9+svQZBZ2ZJnUA/gPbUTFN79DsGpbbExQGZB68uMwjbyu119wMNylerwQIiLqPnriOVmWQnDv+hiOdU8iUPlzk+26AWfDOv4uaPqc2Ou/nyWz6hUPwrH2763vJIjoe80vEA3ZDHOpCVmW4dr5HqqX3gXJVx0bF/VZSDvlORjyZyakrmQ+L/srNsO54Xm47B8CUjBum6A2wTTidzCPuZFdzxJMlmWEnPtjwa6/eDmCNbvbfJ3SOrhh5m7uiVBaBsX9Hq0PjQ99NRvB6vjJHaIxD+nTX4G+/+kN6+DufA/uXR9zHVzq9pL5vJwMGOb2Agxz6Uh4i5aj9qeH4SteHjcuiFqYRv0e1gl3Q9RnJqi65CPLMgIVmyItlPcsbPFLn6DUQdf/zGiAezYUalMXV3rsv3B9pWtQueQGBCu3NgwKIixjb4V10oNQqPQdUSYlMTkcQO2av6F2zePxs3HzpiJ9xmu8UG0Bw93eixdCRETdR086J0shL1zb3oFj/TMIOffFbxREGGyXwTr+TqjTRyamQOowsiwh7C5H0bsjmw8BooxDr0TGmW91YWWUjMLuclT9cDvcuz+OG9cPugBppz4PpSGnS+tJtvOyLEvw7vsajo3Pw3fw+ybbleb+MI+5CaYRV3fZQ/105MLucvhKVkRm75b8iEDFZkCWWn2NaMiJhbuanBMgqE0o/fBESP6a5l8gKGGwXQpf8Y8I1x1odheug0vdUbKdl5MNw9xegGEuHSlZluE7+D1qfnoY/tJVcdsEpR7mMTfCMu5OiLq0BFXYvcmyjMChjXDv/hju3Z8i5Pil2f0EpR76AWfDkD8TugFnQaEydHGl8TriF64cDsKx/inUrn4MctgfG1eaByD9tHnQHTf9mOuk5OQ/tAmVi6+JWxNaUOqRetLfYBp9A2cAHIFEh7uyHI5MwhcEQJYYEnciXggREXUfPeGcHPbVwLn5ZTg3zYPkrYjbJih1MBVcA/PYW3vVche9gSxL8Jf8hPIvLmp2LUjdcTOQed4nEBSqdq3VSOTe8zmqvr8FYXdpbEyhsSJ1yj9gHD63y2byJ8t5WQq64dq+AM5Nzzf7gL8m5wRYxt4C/aALeG2VhCS/E77SVfCV/Ah/8Qr4y9bE3Q9rlkLVZEZ2eygtg2Ac+lsYh10BlXXwUVZM1HmS5bycrBjm9gIMc+loybIM7/5vUPPTwwgc2hC3TVCbYCm8GebC2zp1HddkIcsyAuXr4N79Cdy7FyLk3N/sfoLKCP3AcyIB7nFndKvZqh35CzdYswuVS25qsuaWcdisyLo6uvRjfg9KDnI4iNq1f0ftmv+LW9NFk3sSMma8DpV1UAKr6xm6KtytX9MnWLMbnn1fQZZCUKePjD2kwUC+4/FCiIio+0jmc3KorgiOjc+jbsvrkIPuuG0KbSrMo2+EecyN/I7eg8myBDlQh7qtb8K951NIQTdU1sEwjbwW+uNmxL7nEbVX2FeLmh/vR93Wf8aNa/udhvTT5kFlGdjpNXT383LIVQzn5pdR9/PrTWdgCiIM+TNhLrwV2pyJiSmQOoUc8sNfvi7WmtlXsrLVzghtaVgH90pociZx2QPq1rr7eTnZMcztBRjm0rGSZRmevf9B7U+PxM2qAyJPX5rH3gZL4c0JaQ2cSLIswV+6JhLg7vm0xdYngtoM/cBzowHuDCiUui6utH06+heuLMtwbX8H1cvujbtwUejSkTb1SRhsv+WX0B4uUPEzKhZfi0DFptiYoNQh5cTHYB5zI8O/TtIZ4a4shRH2HkLlN9fAe9ja6krLQKSd8iz0/c/slM/Tm/FCiIio+0jGc3Kgegcc656Ga+f7TWYAiaa+sIy9DaaC3yW8QxB1HVmW4r6DM8SlY+U9+D0qv/sDQo69sTFBqUfK5EdhHnNTp/796q7nZf+hjXBseA7uXR/FPdAMRK67zCOvhXn0jVCa+yWoQupKshRGoHJLdM3dH+EtWtakO0ZLLOPvgXXiA1Cou89EEKLWdNfzck/BMLcXYJhLHUWWJXh2L0TNqkcRrN4Zt02hTYNl/J0wj/5Dj74ZUN+iyr17YSTAdRU1u59CbYF+0Hkw5F8MXb/pEJSaLq70yHXWL9ywuxxVS++Ae9e/48Z1x52OtFNf4DqpPZAcDqJ23ROoXf1Y3I1DTZ/Jkdm4KfkJrK73OdZwV5M7BUpzf5T+6+QW28ZDoUT2hV9A2/dUhvQdiBdCRETdRzKdk30lq+BY9wQ8e//TZJsqbTgs4++CcchlEERVAqojop5GCnpQ89MjcG58Lm7tUE32RKTPeBXqtBGd8r7d6bwsS2F49v0Xzg3Pw1e8rMl2pWUgzGP+CNOIub1uIgTFC7nLcfD1vu3aN/vib6DNm8prbEoa3em83BMdS5jLJv5EvYwgKGAYcgn0gy+Ce9e/ULPqrwjV7gEASL4q1Pz4ABwbnoV1/D0wjbqu285APVKyFIavZAU89QFuo3VhGlNoUqAfdH40wD0Vgqju4kq7J9GQhcyz34Nn2JWo/N/NCNcdBAB4f12M4vmFSDnhIZgLb+baMD1EoHJrZDZuo9bsgqhFyomPwjzmZj75nwCCIEBlHQSVdRBMBVe3K9yVA054930F776vIgOiBmht3R8phKpl9yJv1vpO/CRERETUksgSOYvgWPckfMXLm2zX9DkR1vF3QTfgLN4UJqIOpVDpkTblcRiH/AYV312PYOVWAIC/bA2K35sI68T7YJ1wb4+8RyIFXHBtfxeOTS/G7o81psk9CZbCW6AfeB6vhQkAoDRkQZMzCf7SVa3uJ+qzoM09mb+ziahD8K47US8lKEQYh14Bw5BL4drxHmpXPxZbI1byHEL1srvgWP80rBPvg2nE1UkxK/VwshSCr3g53LsXwrPnM4Q95c3up9CmQT/4AhgGz4Su7yl8ur0V+gFnI2/2yahZ+RCcm+YBkCGHPKhefi9c9n8hffrL0GQWJrpMOkqyFIJj3VOoWf0XIByIjWuyj0f66W9AncrOEN3F0YS7rQa5UcHKLaj84Q6o00ZA1FohqM1QqC1QaEyRn2ozBJWB7dXbSZbCGFkwHJIktb0zERH1WrIUgnvXR6hd92QsQGlMP/AcWMbfDW2fyQmojoh6E032eOT+dlW0S9PfIl2apCBqV/0F7t0LkTHjNWiyJyS6zA4RqiuCc9M81G39JyR/bfxGhRKG/EtgGXsrNFmcnUbxZCkMy9jbcOi/l7e6n3n0jZz0QEQdhm2WkwTbLFNnk8MB1G17B7Vr/q9J22HR1A/WiffDNHxOtw86ZSkE38EfIi2Uf/m8xTUsFLoMGAZfCEP+zEi7kx7y5aorW2H4StegcskN8TecBBGWsbfCOulBKFRcDySZBKq2R2bjlq+LjQmiBimTH4G58FY+gZxkIuHuLw3h7sEfWuxIcMQEEQq1OfIfjTkS+GosUKjrA19T5M8aCwR1Qwis0NS/JhoK98Cn+utFvl/L8B38HiHnASi0KdD1PxMKpRayLDMMJyJKkO7WNk4KelC37S041z+LUN2v8RsVShhtv4Vl/B2d1t6UiKg1gaptqPz2BvjLVjcMCgqYC29BygkPdcjyXIk4L/vL1sGx8Tm4d3/SZD1chcYKU/16uKa8LquJklPNqr+gdtVfmt1mGHoFMs54E4DA6z9KKt3t+3JPwzVzewGGudRVpJAPdVv/CceaxxH2lMVtU1oGwnr8AzAOvaJbhZ9yOAjvwToLJcsAACAASURBVP9FZuD+8gUkX1Wz+4n6LOgHXxQJcHNP7pHhVFf/wpXDQTjWP4Xa1Y9BbjTrT2kZiPTT5kHX77QuqYOOniyF4djwDGp/eiTu/0NN1gSkn/EG1KnDElgddRQp5MOvL5oTXUYcQdRCOCzgbQiJG0Jh4fDx+teoLRDUxm7Zsqpu+3zUrnoUIWfDjXmFxgrz6D/AesKDiFzQd7+6iYh6uu5ycyrsrYJz88twbprX5NpFUBlgKrgGlsJboDT3S1CFREQRshSGc/NLqFnxZ8ghT2xcaRmI9OkvQ9f3lGM6fledl2UpDM/e/8Cx4Tn4S1Y02a60Doal8GYYh82GQm3s1FqoZ/GXrYNz80vwHlgCWQ5DkzUB5tE3QN//DMiyxOs+Sjrd5ftyT8U1c4mowyiUWljG3ATTiKtRt+U11K59Ija7NeTYi8rF18Kx9h+wHv8nGGyXJuxLiRwOwHtgCdy7P4Hnl/9A8tc0u59o6AND/kXQD54JbZ/JPTLATSRBVME68T4Y8meicslN8BUtBRD5u1K28CwYh81C6pR/QNSlJ7hSak6geicqF18X/6S1qEbKpAdhGXdHt3pog46NQqmF7rjT4f11ces7CiJMBddADvshBRyQAnWQ/A7IASckvxNSwAE55O2QmuSwD7LHB8lz6BiOIkRn/5rjQt72zQ6OjAsacyRY7qCnpZ2bX0bV97c2GZf8tahd838IOQ8g48y3OuS9iIgouYScB+DY8Bzqtv4zLhQBAIUuHeYxN8E8+g8QtakJqpCIKJ6gEGEpvBn6geeicsmN8B1YAiB6zf/JGTCOuBqpJz8OUWtNcKXNkwJ1qNv2Npyb5iHk2NtkuzZ3Csxjb4V+wNm8X0RHRZ1ZGJ2B20CWwgDAIJeIOhTv0hJRsxQqPSxjb4Op4Fo4N78Ex7qnYoFpsGYXKhbNQe3avyNl0oPQD76wS76gSCEffAe+iwa4X0IKOJrdTzTmwZA/E4b8mdDkTOKXpy6gShmC7IsXw7XtbVQvvze23oxrxwJ49i9C2tQnYbD9lq1luglZCsO58XnUrHwwbjauOnMsMs74J1v59UCyFIa58OY2w1zTiKuQftqLrR8rHIQUcEb+448EvnLAEf1z/Zgzbh85Ggo3HoMc7ohPBjngRDjgRNh1DIdRqJqZHdxcC+loINxo38b7RNYQv7/Vt3LtfA+mUddBk308bxh1kPqbJRAU/D1DRN1SoGobHOuegsv+YZOWnkrTcbCMux3GEVdxmRIi6rZUlgHIvugruLbPR/Wyuxqu+be9Be/+RUg75XkYBl+Q4CobhJwH4IiuhysHnPEbFUoYhlwaWQ83szAxBVKP0dw1Ha/ziKgzMMwlolYp1EZYJ9wD86gb4Nj4PBwbno19EQ5Wbceh/14OdcZopJzwEHQDzmnxJqosy4AsHfGNVinkhXf/Yrj3LIRn75eQA3XN7iea+kUD3IuhyZ7AADcBBEGAqeBq6Aacheof7oB798cAAMlbiYpFV8G1432knfoiVJb+iS20lwvW7ELF4uvgL/2pYVChQsqkP8My/i7Oxu2hBIUIff8zkHLCI6j56aFm99HmnoTUaU9DlsKtXnwKogqiLg2iLu2o65FlGXLIG5n963dGZv4GGgXB0VnATWcGHzYePJYEtxEpCMlbCclbeWzHUagBKdDmblXf3w7TqOuhUBkgqAxQqI1QqIwQVHoIKmN03AhB1DCcbEH939Owrwa+4hUAZGiyJ0BpyG7z7zARUVfxlayEY90T8Oz9b5NtqvQCWMffBUP+byCIqgRUR0R0ZARBgGnEHOj6n46q72+FZ8+nAICwuxSHvvwNDPkXI23asxANWQmr0Ve6Bs4Nz8K959MmD48qNCkwjboO5tF/gNKYm6AKiYiIjg7XzE0SXDOXuouwrwaODc/AufHFJjfRNVkTYD3hIeiOmxG7+Rw5x0jwF69E2O+AytIf6vSCVm+0SkEPvPsXRdbA3fdVizfrleYBMORfBEP+JVBnjeMNb3SvdQ08e/+Lyu9vQbjuYGxMUOqRMvlhmMf8kaFhF5NlCc6NL6JmxZ8gh32xcXVmITJOfwPq9JEJrI66kq94BRybXoR339eQpQDUGaNhHnk9jMNmRR64SaIQTJbCkWA3cFjgG50xHAmLozODo8Fxw+zghnAY4bYD2IQQxEjYqzJCoTZAUBogNAp+Iz8NsfA3/mckJBaUhshre1BIHPn/3YHqpXfDveujhg4Dggj9wHORNu1piIY+SfV3mYi6Rld8V5ZlCd59X6F23ZPwl6xssl2bOwWW8XdB1/+MpD4XExG5dy9E1fe3Iuwpj40pNClInfoEjMNmt+sc1xHnZVkKwbPnMzg2Pg9/6aom21Up+TCPuQXG4bOgUBmO+n2IiHqD7nRvuSfimrlE1GVEbQpSJz8KS+EtcKx7Es7NL8fWT/SXr0X5Z+dC02cyrJMegr7fKXBufB6ODc8h7CqKHUOdNQ4pJzwCff/TY2NS0A3Pvq/g2b0Qnn1fN1lDqp7SMigyA3fIJVBnjOENkG5MP/Ac5OVNQc3Kh+DcNA+AHGlBuuweuHZ+iPTpr0CTOSbRZfYKwdo9qPj2eviLf2wYVChhPf7/wTr+Hs4G6WU0OZOQlXti3JgshyEIyRd8CQoRotZ6zGt0SSFfNAxuHAK3NDu4Yaw+GJajwTDQwQ9JyuGGNtLuDjxuSyGx0gBBbejWIbEsS5CDLpT+ayqCNfbDNobh+eVz+MvWoM/lPzLQJaIuJYeDcNk/hGP9UwhWbW+yXT/ofFjG3w1tzvEJqI6IqOMZ8mdC2/eUyDX+9ncAAJK/BpWLr4V757+Qdtq8Tu3MJfmdqNv2FpwbX0So7tcm27V9T4Gl8BboBpzF7m1ERJT0GOYS0VERdelIPfnvMI+9DY61T6Buy2uxmTH+kpUoX3gGREMfhN0lTV4bKF+P8s/PR9qM16BQqOHesxDe/d/EQuHDqVLyoc+/GIb8i6FOH8UAN4ko1CakTXsaBtvlqFxyA4KVWwEAgUMbUPLBCbCMvQ3WSX/m+mCdRJYlODe/jJofH4j796VOH4X0099gmN5LNbumTxIGuR1JodQCSi1EfeZRH0MKB+He8ykqv57V5r7qrPFQp4+EHHRBCrohB92Qgq7oTzfkgAtSyN15M4a7ICSOzRxuV0gcbS/djpBYEBSoXv1Y0yC3kbC7FNXL70fm2Qs68MMRETVPCrpRt/VNODY8G9eRBgCgUME49ApYxt8BdeqwxBRIRNSJRG0KMk5/HUbbZahcciNCzv0AAO+Bb1G8oBApJ/4V5tF/6NAwNejYB+emeajb9lbTpbgUKhhtl8NceDOvd4mIqEdhm+UkwTbL1N2F6opQu/Zx1G19E5CCx3w8VepQGPIvhiF/JlRpBQxw26k7t8KQw0E41j+F2tWPNbTEBKC0DET6afOg63daAqvreYKOvahcfD18xcsaBhVKWCfcB+vE+yCI6sQVR9RDybKEoreGIeTc1/JOChX6/m4XRENOmze15HAwGva6omGvJ/qzcQgcDX9b3N4oJI7+udu2lW6JoIiGu5GQOOTY12QNtKavUSLv6h1QmY/rmhqJKCl05HflsLcSzk3z4Nz8MiRfddw2QWWEaeR1sBTeDKUp75jfi4goGUgBF2p+ehjOjS+gcccaTc4JSJ/xSrMPtTgcDgiCALPZ3OqxZVmGv3QVHBueheeXzwFZituu0KbBPOp6mEbfAKUhp0M+DxFRb9Sd7y33BGyzTEQJpzTlIf3UF2AZfxdqV/8Nrm1v40jbTarSRsQCXHXa8E6pkxJHEFWwTrwPhvyZqFxyE3xFSwEAIcdelC08C8Zhs5A65QmIurQEV5rcZFlC3c+vofrH+yEHG6bcqdILkHH6P6HJLExgdUQ9nYzM8/6Nsk/OgOSrarpZoUTG6f+E0pjbrqMJogqiaAWOsY10kypbDImbmSHcWoh8WGjceTOJpdhM4va/JoSiN/MhqE1QGvpANPaBaMiB0pAD0dgHSmMfiIaGMUGp6ZzaiajHCTp/hXPDs6jb+maTzkIKXQYshX+EadQNELUpCaqQiCgxFGoj0qY+CcOQ36Dy2+sRrN4BAPCX/oTi9ybAOvEBWMffDShEAAIgS9D49gAhP8LqoRC1qZClcFwnIVkKwb17IZwbnoO/fG2T91Sl2GAeeyuMQ69gxy8iIurRGOYSUYdSmY9DxoxX4Tv4fay9TmsU2jRYxt4C/eCLoE4d2vkFUsKpUoYg++LFcG17G9XL74XkrwUAuHYsgGf/osjFn+23nI19FIKO/aj87vfwHfy+YVAQYZ1wD6zH/z/OxiXqZIIgQp1egNwr18Cx/hm4dsyH5HcAChUMgy+CZdxt0GSNT3SZXRQSHz4zuLWQ+PAZxB0XEsuBOgQD9lbbMgOR7yOiMQdKQ270Z04k7DXmQGnMhWjIgajPhKDg5RNRbxWo3ILadU/Cbf+oSXcApXkALONuh3HEXCiUugRVSETUPWhzjkfuFWtQu/bvqF37OCCFgHAAtT89DPfuT5B+2svwHVgC58+vNizNJaphGHwRUib/BUpzP0h+J1zb3oRj07ymLewBaPtNj6yH2/90rodLRES9Au9GEFGnENp5E8NguxTWifd3cjXU3QiCAFPB1dANOAvVP9wB9+6PAQCStxIVi66Ca8cHSDv1Bags/RNbaJKQZRl1W95A9fJ7IQddsXFV6jBknPEmNFlsjULUVQRBAdGYi7RpTyN16hOQ/HWRtV9FFWSpjdbASa6rZhKXfXI6wu6ytqoBRDXQqK1/ayRfFSRfVWxt9+YPqYCoz4rM5q2f2WvMicz8rR8z9oFCm8YHkoh6CFmW4Sv+EY51T8K7/+sm29UZo2EZfxcM+RfzYQ8iokYEpQYpJzwEQ/5MVHz7ewTK1wEAgpVbUPqvk5q+IByA2/4vePYvhmHgOXDv+Szu2hYAIKoj65AX3gx1+sgu+BRERETdB682iKhTaPtOi7XUaY2u7ylN2uhQ76E0ZCPznPfh2XslKv93M8KuIgCA99dvUDx/DFImPwzzmD/y5lgrgs5fUfndDfAdWNIwKChgGX8XUo7/M1uHEiVA/ewAQRBR5QxAknzIzs7m77qj1DgklmUJ5sJbUPPjA62+xjz6D0id9gwkfw3CrmKEXKUIu0sRdpdE/3sJQq6S6FhZ22vwAoAsRfcvReDQhpb3E9VQ6nMgGqMtnaNhbyz4jc4AFtQmhr5EXax///5QKNqewSXLEjx7v4Rj7RPwl61usl2bNw2W8XdBd9wM/jsmImqFOn0k+ly2HM5NL6Bm5UNN2tMfTvbXwLVjQdyYQpcB86jfwzzq9xANWZ1ZLhERUbfFu+NE1OFkWYJ59I2o2/wKWls3VzTmQT/oPAgCb273dvqB5yAvbwpqVj4E56Z5AGTIIQ+ql90D184PkT79FWgyxyS6zG5FlmW4tr2FqmV3Qw7UxcZVKbbIbNzsCQmsjojqHTwYaQuXnZ2d4Ep6CgGWsbfBV7QU3v3fNLuHOmscUk56DIAEUZsKUZva6uwNWQoj7K2IBLWx4LcEIXcpwq7omLsUkreifSWGAwjV/YpQ3a+tfxKVIbpm72HtnGMzfyNhcCJbttY/cBf2ViLsqYCoz4CoS+eDeJR06v/OWjTByEMefgGixtp0bcZwAK6dH8Kx/kkEq3cedhQB+sEXwjr+bmiyE98yn4goWQgKEZaxt0E/8DwUvzeh6YzbFqhSh8Ey9jYYhv4WCqW2k6skIiLq3npEmGuz2aYDeADAaAAqAOsBPG632xcdwTEmAfgTgMkAjAAOAvgCwKN2u72mmf2HA3gQwKkAzACKAHwK4P/sdnv1MX0goiQnCAqoU21InfIPVC+7u/l9VEZknv0eAD7JThEKtQlp056GwXY5KpfcEGt1GTi0ASUfnADL2NtgnfRnKFT6BFeaeKG6IlR+dwO8vy5uGBQUsIy9HdYTHuKFLhH1WIIgQJYFZJ3/KZyb5sH586sI1e4BAIjGXJgKroVl3O0QRE27HxYTFCKUhmwoDdlAZmGL+8nhAMLuskjIWz+zNzq7NxL8Rv4sBRztel856Eaodk+s/pYoNCmRWb6GPpG1fI19GsJeQx8ojTkQ9dkQRFW73vdI+Et/Qu3ax+HdvxiRB/QE6PqfDuuEe6HNbaZFIlE35d2/CI71T8FX/GNkQKGEfuD5sB7/ANTpIyEHXajb+k84Njwf6xQTI6phHDYLlrG3Q51q6/riiYh6CKUpr91BrmnMH5F68uNQdML3GyIiomQkyHLLs+aSgc1muwrAWwD8AP4HQARwCiKh7u/tdvtr7TjGBQA+RiTcXg2gDMAEAH0A7AEw2W63VzTafxSAlQAMAH4G8AuAUQAGIRICT7Lb7SUd8wkj1q9f/wOAqUajETYbLyApeXj2fQ3H+qfhK1oKABBEDfT5F8M68T6oUobEWlFSx1i/fj0AYNy45F4jVQ4H4Vj/FGpXPwa50XqHSstApJ82D7p+pyWwusSRZRmu7e+iaumdkAPO2LgqJR/pM/4JbZ9JCayOiJrTU87L3ZEsSxAEBUKuEkCWIBpygFiL68Q9LCYF3ZGANzrDN+wqaQh7Y8FvcZttBo+MAFGfGZvNWx/6xsLe6AxghT6j3d+9XLv+jYqv5zTfgloQkXHmOzDaLu3Az0DUORwbX0D10jub3yhqYcifCe++ryD5a+M2CWoTzCOvg7nwFiiNfbqgUiKink0O+7H/BVO79k077SWYRlzFJZeIiLoY72F0LrvdDpfLBQBLx40bN+1IXpvUvxFtNlsfAK8AcAA4yW63b42OTwDwHYDnbDbbf+12e3Erx1ACeBWAAsDFdrt9YXRcC+AjAOchMgP35kYv+xCRIPc2u93+XHR/EcA8AL8H8DiA2R34UYmSlu6406EfcBbC3kpIfgdEfRYUamOkpRmDXGqBIKpgnXgfDPkzUbnkptjDACHHXpQtPAvGYbOROuUfEHVpCa6064Rcxaj87kZ493/daFSAeextSJn8cELbcBIRJUL994juFrIoVAYorIOhsg5ucR9ZliH5HZGw11162CzfEoRdpbEZwJBC7XhXGWFPOcKecqBiUyvFKaPBbk6T4Le+3bNCnwNBUKBy8bUtryUsh1G5+FrojjsdCo2Fa4ZStyTLEkLO/aheelfLO4V9cO98P25I1GfBXHgzTCOvh6i1dnKVRES9hyBqoM4aj0D5ujb31eVNiT2kR0REREke5gL4IwANIq2Nt9YP2u32tTab7XEAjwG4HsBDrRxjFIAsABvrg9zoMXw2m+0viIS5U+rHbTbbWADDAGyuD3Kj+4dtNtufEQlzz+yID0fUE9SvQSXq0iHq0puME7VGlTIE2Rcvhmvb26hefm9sxoRrx3x49i9C2tQnYbBd3qNvIsuyDNeOBaj+4Y641p1K62BknP4GtH0mJ7A6IiI6GoIgQNRaI0FR2vAW95NlCZK3MhL2ukvjgt9QNAgOu0ojIS7a0XFJCiFcdxDhuoOt76dQAVKw1V3ksA91W/8Jy7jbwWUzqDsSBAWcm15Cu/5tINIBxjLuThiHz+aSFUREnUCWwjCPviHywFgrtHnToEoZ0kVVERERJYdkD3PrQ9PPmtn2GSJh7lloPcyVoj8zbTab0m63N370vT55iq2Ba7fbN9hsthxEZuYerr5XSHsenycionYQBAGmgquhG3AWqn+4A+7dHwMAJG8FKhbNhWvH+0g79QWoLP0TW2gnCLlLUbXkRnj2/rfRqABz4c1Imfwo1w8mIurhBEERaZ+szwQwpsX9ZCmEsLusIextNMO3cfAr+apbPEacNoLcer6ipbCOb6F9LVE34Dv4Q7v20/adhuyLvuYDp0REnUhQiDAOmwXPL1/C80tzt3IBhT4T6dNfjnRz4zmZiIgoJmnDXJvNJgAYjkgYu6OZXXZFt42w2WyC3W5v6XHcbYisc9sXwHybzfYnAKUATgDwUvQYTzd+gd1uL2umHguA56N/nH/EH4iIiFqlNGQj85z34dl7JSr/dzPCriIAgPfXb1A8fwxSJj8M85g/9og1dWRZhtv+Aaq+vx2SvyY2rrQMRPqM16HLOzmB1RERUXcjKJRQmvKgNOVB08p+UsgXnc1b0hD2uhuv6VuCkKsEctDdrveVpRbaMBN1E5Istb0TAKW5P0MDIqKuIMvIPPcDONY/g7rNryBUdwBApAWzYchvkHLCwxBNeVyWi4iI6DCCLLev5VB3Y7PZUgFUAaiw2+2ZLexTDiATgMVutztbOdZEAAsB5B62qQTAVXa7/dtWXjsLwFxEwl89gDcB/MFut7fvcfZ2Wr9+/Q8ApnbkMYmIklbIDf2vr0JT8jGERq3zQsahcOffj7AxeVsyCYEqGPb8A+qqZXHjvj6/gaf/HwCRa+MSEVHnUKlUGDlyJGpW/hmOtf9oc3+luT+yLvgMO4tDCAY79PKH6Jjo9XoMTHGh7PMLIHkr29w/7bSXYBx+FTZt/hnJeo+EiCgZKBQK9OvXD6mpKYAsI1C9A3LIB5V1MEStFV6PG/t/PQCPx5PoUomIiDrT0nHjxk07khck82NO9W2OW/vt7o3+NLZxrD0A3gMQBrAGwJeIzM7tA+CeaHDcknMATI/WIwFIQ2SWLxERdRalAZ5Bd8A5+jWE9AMbhl07Yd74O+j2zQPCvgQWeBRkGepDi2FZf0VckBvW5MA5ch48g+5gkEtERJ0qGAzC6XTAPOZmoB2dLkLO/SheMB5K+1NAqH2zeYk6mxCohrDp/6HkwxPbFeQqNBYYh16J6ppaBrlERJ1MkiTs378fW7ZsRVn5IXiVefDr8lHjCsFut2P7jp0McomIiJqRzL0o63t6tedqq8XQ2mazpQH4EZFZudPtdvsP0XENgBcBXAvgU7Q8K/YOAFcBGADgAQCzAUy02WwFdru9poXXHDWj0QibzdbRhyWiHmL9+vUAgHHjxiW4kq4yDvKU38Kx/mnUrn4MctgPAWHoihbAVLcS6afNg67faYkusk1hzyFU/u+P8OyJXzfINOoGpJ70NyjUbT2TRETdVe87L1NPkXry46he2tp6uAIAGZBD0BV/AGPN/5By0mMwDpvF1oiUELIUgnPzy6jd+CikgKNhgyACcgstwQUF0k6dB4VKh/R0HdLT07umWCIiAhD/XZnnYCKixOM9jM5lt9vhcrmO6rXJfJVd/+h3a9OU6re19r/O3QCGAvhLfZALAHa73Q/gRgB2AFNsNluzCxTa7fZSu93ut9vtO+12+xwAXyMyo/eGdn0KIiI6JoKohnXifcidtR7avIbnbkKOvShbeBYqvrkGYW9VAitsnXvXxyiaPyYuyBVN/ZA9cxHST32eQS4RESWEpfBmZJy1AKq0grhxVVoBMs5agNwr10Kb23CJFPaUo3LxtSj9aBr8hzZ2dbnUy3kPLkXxexNQvfTOuCBXN/Bc5F21DWmnvgClZWDcazTZE5B1wRcw2i7t6nKJiIiIiIiOSDLPzHUiEuim22w2pd1uDzXeaLPZlADSAfjsdnttK8eZFv3ZZF1cu90etNls3wGwASgEsLwddb0H4Kzo/kRE1EVUKUOQffFiuLa9jerl90LyR079rh3z4dm/CGlTn4TBdjkEQUhwpRFhTwWqvr8F7t2fxI2bCq5F6sl/h0JjTlBlREREEYYhl8BouxT+Q5sQ9pRB1GdDkzkGsixBEBTIvuQ7uHd9hOrl9yHsKgYA+EtXoeT9STAVXIOUEx+FqOMsG+o8obqDqF52L9y7P44bV1oHI23q09APODPy55HXwTzq9/CVroHkr4bS3B/q1KGQpRZm7BIREREREXUjSTsz1263ywC2ARABDGlmFxsin29LG4eyRn+GWtheP64GAJvNdqrNZnvDZrP9toX9/dGfqjbel4iIOpggCDAVXI3cOT/DkH9JbFzyVqBi0VyUf3Y+go79iSswyr17IYrmj4kLckVTX2Rd9F+kT3+JQS4REXUL9e2S1Rmjoes3A+qM0XHjgiDAaLsMeXO2wDL+bkBRfwkko27rGyh6ewScm19hYEYdTg75Ubvm7yh6Z2RckCuoDEg58THkzdoYC3KBhr+zQeMwyOknQp06NDKuELu2cCIiIiIioqOQtGFu1KLozwub2VY/9lUbx9gZ/Xn24RtsNpsI4NToHzdHf2YDuAbAbS0c76zozw1tvC8REXUSpSEbmee8j6zzF0I05sXGvb9+g+L5Y+DY8BxkqaVneDpP2FuFQ1/PxqH/Xg7JWxEbNxb8DnmzNkB/3Iwur4mIiKgtgiBAUIgtdrdQqI1IPekx5M3eBF3/hgBN8teg6vtbUPL+8fAVr+iqcqmH8+z7CkXzx6Bm5YOQQ57YuMF2GfLmboV1wt0QlJpmX7tr1y7stO/qqlKJiIiIiIg6RLKHuW8B8AG412azxVZkttls4wHcA8AL4KVG44NsNtt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      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 484,
       "width": 953
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# translate model names\n",
    "rcParams['figure.figsize'] = 16, 8\n",
    "sns.scatterplot(data=df, x='density', y='val_acc_max', hue='model')\n",
    "sns.lineplot(data=df, x='density', y='val_acc_max', hue='model', legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
